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Can the perplexity metric, whether high or low, truly reflect the strengths or weaknesses of a model? Is it possible that relying on this single measure oversimplifies the complexities of model performance? Should we not consider additional factors to gain a more comprehensive understanding of a model's effectiveness?

Can high or low values of perplexity indicate specific strengths or weaknesses in a model?

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Can high or low values of perplexity indicate specific strengths or weaknesses in a model?

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